IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 127- 132

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Real-time Transmission of Layered MDC Video over Relay-Assisted Network S.V. Krishnachaitanya1, S.Manikandaswamy2 1

Student, Department of Electronics and Communication, SRM University, Kancheepuram, TN 603203 India e-mail: [email protected] 2 Assistant Professor, Department of Electronics and Communication, SRM University, Kancheepuram, TN 603203 India e-mail: [email protected],.

ABSTRACT The Internet is growing quickly as a network of heterogeneous communication networks. The number of users is rapidly expanding and bandwidth-hungry services, such as video streaming, are becoming more and more popular by the day. However, heterogeneity and congestion cause three main problems: unpredictable throughput, losses and delays. The challenge is therefore to provide: (i) quality, even at low bitrates, (ii) reliability, independent of loss patterns and (iii) interactivity (low perceived latency) to many users simultaneously. Multiple Description Coding (MDC) has been proposed as a solution to real time video delivery over relay assisted wireless networks and also for unreliable channels by means of independent descriptions where as on other hand Layered MDC addresses the problems of heterogeneous line bandwidths and dynamic network congestion by means of sequence of layers. In this paper we explored layered multiple description codes, which have the advantages of both layered codes and multiple description codes by maximizing the benefits of relaying for mobile video streaming. Keywords: Layer Coding, Multiple Description Coding, Multipath wireless relaying, Relay assisted network. 1. INTRODUCTION To enable seamless and uninterrupted real time multimedia transmission to heterogeneous mobile clients, it is necessary to exploit different diversity techniques to rectify severe noise and fading effects of wireless channels. Multipath diversity together with multiple description coding (MDC) [1] is an efficient technique to combat noise, fading and shadowing in mobile channels. To autonomously and dynamically adapt to network bandwidth, delay, and channel noise fluctuations, a promising solution is to combine multipath relaying with MDC [1], [6]. MDC generates multiple descriptions of the same source data that can be independently decoded and is very well suited to emerging relay-assisted networks, where each description can be routed via a different relay exploiting network diversity. However, since mobile channels are very prone to packet losses, forward error correction (FEC) is still necessary to ensure that enough descriptions are correctly received. To provide adaptive resilience to packet drops, which are frequent in mobile channels, application layer FEC (AL-FEC) has become very popular. Indeed, AL-FEC via Raptor codes [7] has become standard for Digital Video Broadcasting-Handheld (DVB-H) and Multimedia Broadcast Multicast Service (MBMS) [8]. Another class of erasure codes recently developed is Random Linear Codes (RLC) [9], [10], [11]. Like Raptor codes, RLC are rate less, capacity approaching, and of low encoding complexity. However, rooted in the network coding principles (see [9] and references therein), RLC enable simple and efficient relaying and network cooperation. In this paper, we propose a system for real-time video streaming to mobile users, via, relaying wireless systems, using layered MDC [12] together with RLC. The source data is encoded in multiple descriptions, which are sent to a receiver over noncooperative relays. The receiver can reconstruct the encoded data from any subset of the descriptions received. To maximize benefits of relaying and MDC, we propose two algorithms for optimal and suboptimal, but fast, relay selection and optimal resource allocation that minimizes reconstructed source distortion. Multiple descriptions are used in conjunction with expanding window RLC (EW- RLC), originally developed in [15], enabling flexible rate adaptation and competitive performance [16]. The focus of the paper is maximizing benefits of relaying for mobile video streaming. The main contributions are: (1) an adaptive scheme for multiple-relay layered MDC communication; (2) optimal relay selection and optimal source channel rate

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allocation via dynamic programming; (3) multipath EW RLC as a robust solution to unequally protecting the data of each description. The rest of the paper is structured as follows. The related work is described in Section 2. In Section 3, the employed system model is shown. Section IV describes our proposed system and its resource allocation algorithms. Section 5 describes the simulation results. Performance evaluation and conclusion is described in the last section.

2. RELATED WORK In this section we review related work. The first category of related work is contributions to resource allocation and scheduling in relay networks. Information-theoretical bounds of relaying, power allocation protocols, and error control code designs are proposed in [17], [18] and references therein. The relay selection problem using statistical analysis based on instantaneous channel characteristics at the physical layer is considered in [19]. In [20] a physical-layer relay selection method is proposed that minimizes the multiplexing loss with short feedback and decode-and-forward relaying. Resource allocation in relay-assisted communication has also been studied extensively. In for an orthogonal frequencydivision multiple-access (OFDMA) base relaying system, the optimization of physical-layer transmission protocols is done using a set of pricing variables as weighting factors. In an amplify-and-forward wireless relay system is considered, and novel power allocation strategies are proposed, based on geometric programming, that optimize the maximum transmit power and the network throughput. In [14] a dynamic resource allocation of transmission powers and sub-channels under power constraints based on instantaneous channel states that maximize the instantaneous total transmission rate is investigated and a greedy approach proposed. In [19], a globally optimal Pareto-based solution and sequential optimization algorithm using channel state information are proposed for the OFDMA-based half-duplex single-relay channel. The second category of related work covers the path selection and resource allocation for multipath video. In [16], optimal and polynomial-time suboptimal algorithms for selecting the network path and scheduling packets of encoded scalable video are proposed that minimize video distortion and time delay at the receiver. In [17], an MDC relaying is considered, and an adaptive compression scheme is proposed at the source and relay based on the received feedback. In [18] a joint source-channel scheme is proposed that exploits information about packet losses to adaptively select reference frames in MDC. In [19] FEC and routing are jointly optimized to maximize reconstructed quality. The main difference between our paper and the above contributions is that our resource allocation strategy considers both lower and upper layers parameters in a joint cross-layer design and proposes a dynamic programming and a suboptimal greedy solution. In contrast to [18], [19], we do not alter video encoding, making it compatible with the standard, but only adapt the coding rate and relay selection based on the channel characteristics. In the proposed scheme, descriptions are generated similarly to [20], however, we create independent descriptions with least possible duplication using slicing as well as data partitioning [12]. 3. MULTIPLE DESCRIPTION CODING Multiple Description Coding (MDC) can be seen as another way of enhancing error resilience without using complex channel coding schemes. The goal of MDC is to create several independent descriptions that can contribute to one or more characteristics of video: spatial or temporal resolution, signal-to-noise ratio, frequency content. Descriptions can have the same importance (balanced MDC schemes) or they can have different importance (unbalanced MDC schemes). The more descriptions received, the higher the quality of decoded video. There is no threshold under which the quality drops (cliff effect). This is known as “graceful degradation”. The robustness comes from the fact that it is unlikely that the same portion of the same picture is corrupted in all descriptions. The coding efficiency is reduced depending on the amount of redundancy left among descriptions; however channel coding can indeed be reduced because of enhanced error resilience. Experiments have shown that Multiple Description is very robust: the delivered quality is acceptable even at high loss rates. Descriptions can be dropped wherever it is needed: at the transmitter side if the bandwidth is less than expected; at the receiver side if there is no need or if it is not possible to use all descriptions successfully received. This is known as “scalability”. It should be noted that this is a side benefit of Multiple Description Coding which is not designed to obtain scalability; instead it is designed for robustness. Descriptions of the same portion of video should be offset in time as much as possible when streams are multiplexed. In this way a burst of losses at a given time does not cause the loss of the same portion of data in all descriptions at the same time. If interleaving is used, the same criterion is to be used: descriptions of the same portion of video should be spaced as much as possible. In this way a burst of losses does not cause the loss of the same portion of data in all descriptions at the same time. The added delay due to the amount of offset in time, or the interleaving depth, must be taken into account. 4. LAYERED CODING Layered Coding (LC) is analogous to Multiple Description Coding (MDC). The main difference lies in the dependency. The goal of LC is to create dependent layers: there is one base layer and several enhancement layers that can be used, one after another, to refine the decoded quality of the base layer. S.V. Krishnachaitanya, IJRIT

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Layers can be dropped wherever required but they cannot be dropped at random: the last enhancement layer should be dropped first, while the base layer must never be dropped. If the base layer is not received, nothing can be enhanced by the successive layers. Layered Coding is designed to obtain this kind of scalability. Repair mechanisms are needed to guarantee the delivery of at least the base layer. Moreover: because of the unequal importance of layers, repair mechanisms should unequally protect the layers to better exploit Layered Coding. However not all networks offer this kind of services (prioritization). 4.1 Recovery mechanisms and Layered / Multiple Description Coding Channel coding is needed by Layered Coding. However channel coding can also be used with Multiple Description Coding. Generally speaking, it is better to adapt the protection level of a given description / layer to its importance, a technique commonly known as “unequal error protection”. Unequal error protection is better even in the case of equally-important descriptions (balanced MDC). In fact, armouring only one description may be more effective than trying to protect all descriptions. If this is done, there is one description which is heavily protected. If the channel becomes really bad, this description is likely to survive losses. Then the decoder will be able to guarantee a basic quality, thanks to this description. 4.2 Design metrics There are a number of design metrics that determine the performance of a sensor network. These metrics include energy efficiency, latency, accuracy, fault tolerance and scalability. • Throughput: Throughput is defined as the amount of data successfully delivered within a unit time. • Energy efficiency. In many scenarios, nodes have to rely on a limited supply of energy (e.g., batteries). When sensors are battery operated, it is usually not practicable to simultaneously replace or recharge these energy sources in the field. It is wise to manage energy to extend the lifetime of the network. • Latency. Many sensor applications (e.g., multimedia networks) require delay guaranteed service. In these applications, sensed data must be delivered to the user within a certain delay. • Scalability. Scalable routing algorithms can operate efficiently in a wide range sensor network, which contains thousands or hundreds of thousands of nodes. Network performance must not significantly degrade as the network size or node density increases. How to design such routing protocols is very important to the future of sensor networks.

5. SIMULATION SETUP 5.1 Simulation Parameters Table 1: Simulation Parameters Parameters

NS2 Version Simulation Time Transmission Power Received Power Idle Power MAC Protocol Bandwidth Initial Energy

Values

NS-2.38 1000sec 66mW 39.5mW 3.5mW 802.11b 11Mbps 14J

5.2 Video Coding and AL-FEC Error resilience via slicing splits the frame into multiple slices that are independently encoded and separated by resynchronization points. Since slices can be made independently decodable, the partitioning of a frame into slices can be used to create multiple descriptions with fine granularity. For example, D =2 descriptions, namely, MDC1 and MDC2, can be created by assigning three slices from each frame to each description. In this way each description has a significant representation of every frame which helps the decoding process. The way the slices are chosen for inclusion in a description is shown in Fig. 1. Firstly, both descriptions have the intra-coded Instantaneous decoder refresh (IDR) [11] as the first entry. Thereafter, starting from the first following frame, alternate slices are copied to each description. This way, the overlap in the source content between two descriptions is minimal.

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Fig. 1 Generation of two Descriptions To achieve scalability, each description is encoded using data partitioning (DP), which enables partitioning of each slice into up to three partitions (denoted as A, B, and C) based on the importance of the encoded video syntax elements to video reconstruction. Data partition A (DP A) contains the most important data comprising slice header, quantization parameters, and motion vectors. DP B contains the intra-coded macroblocks (MB) residual data, and DP C contains interceded MB residual data. The decoding of DP B is made independent of DP C, using the Constrained Intra Prediction (CIP) parameter in the H.264/AVC encoder. The effect of error propagation is limited by inserting periodic macroblock intra updates (MBIU) [11]. This way, each description can be coded using up to three quality layers. After video encoding, two independently decodable descriptions of possibly different sizes are generated. Each description contains l = 2 quality layers obtained by grouping IDR + DP A + DP B into one layer (i.e., the base layer (BL)) and DP C into the second quality enhancement layer (EL). 6. PERFORMANCE EVALUATION All the simulations have been performed in the NS-2 simulator with varying number of nodes. The Application videobroadcasting is taken and simulated for the packet delay and packet loss rate. 6.1 Packet Loss Rate Packet loss occurs when one or more packets of data travelling across a computer network fail to reach their destination. The figure shows how multipath relay assisted scheme decreases the packet loss rate. This improves the network efficiency.

PACKET LOSS RATE PACKETS(kbps)

0.4 0.3 0.2 0.1 0 0

10

20

30

40

50

TIME(ms)

Fig 2: Graph Indicating Packet Loss 6.2 Delay Evaluation End-to-end delay refers to the time taken for a packet to be transmitted across a network from source to destination.

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Fig 3: Graph Indicating End to End Delay 7. CONCLUSION This paper addresses the problem of real-time transmission of layered MDC video over relay-assisted paths. We designed an MDC scheme using slicing and data partitioning features and fed the resulting packets into the EWRLC encoder for erasure protection. The encoded packets were streamed over a direct link and over multiple relay-assisted channels. A resource optimization framework was developed, using dynamic programming and the proposed fast algorithm that optimally and suboptimally select relays and schedule packets for transmission. We show the advantage of the proposed schemes over high loss rates and fading channels.

References [1] J. G. Apostolopoulos, Reliable video communication over lossy packet networks using multiple state encoding and path diversity," in Proc. SPIE Proc. Visual Commun. and Image Proc., vol. 4310, no. 1, 2001, pp. 392-409. [2]LTE-Advanced,3GPP Global Initiative, http://www.3gpp.prg/LTE-Advanced, 2010. [3] S. Parkvall, A. Furuskar, and E. Dahlman, \Evolution of LTE towards IMT-advanced," IEEE Commun. Magazine, pp. 84{91, Feb. 2011. [4] Y.Yang, H. Hiu, J. Xu, and G. Mao, \Relay technologies for WIMAX and advanced mobile system," IEEE Commun. Magazine, pp. 100{105, Oct. 2009. [5] H. Luo, S. Ci, D. Wu, J. Wu, and H. Tang, \Quality-driven cross-layer optimized video delivery over LTE," IEEE Commun. Magazine, pp. 102{109, Feb. 2010. [6] S. Mao, S. Lin, S. Panwar, Y. Wang, and E. Celebi, \Video transport over ad hoc networks: Multistream coding with multipath transport," IEEE J. Sel. Areas in Communs., vol. 21, no. 10, pp. 1721-1737, 2003. [7] A. Shokrollahi, \Raptor codes," IEEE Trans. Inform. Theory, vol. 52, no. 6, pp. 2251-2567, June 2006. [8] \ETSI TR 102 591, digital video broadcast (DVB); IP datacast: Content delivery protocols (CDP) implementation guidelines part 1: IP datacast over DVB," ETSI Tech. Spec., 2010. [9] D. Lun, M. Medard, R. Koetter, and M. Erros, \On coding for reliable communication over packet networks," Physical Communication, vol. 1, no. 1, pp. 22-30, 2008. [10] J. Jin, B. Li, and T. Kong, \Is random network coding helpful in Wimax?," in Proc. IEEE INFOCOM 2008, Phoenix, AZ, April 2008. [11] H. Shojania and B. Li, \Random network coding on the iphone: Fact or ¯ ction?," in Proc. NOSSDAV- 2009, Williamsburg, USA, June 2009. [12] T. Wiegand, G. Sullivan, G. Bj¿ntegaard, and A. Luthra, \Overview of the H.264/AVC Video Coding Standard," IEEE Trans. Circuits and Sys. Video Techn., vol. 13, no. 7, pp. 560-576, July 2003. [13] Q. Zhang and S. Kassam, \Finite-State Markov model for Rayleigh fading channels," IEEE Trans. Com-mun, vol. 47, no. 11, pp. 1688{1692, Nov. 1999. [14] A. Agrawal, \Heterogeneous networks. A new paradigm for increasing cellular capacity," http://netseminar.stanford.edu/seminars/01 29 09.pdf, Jan. 2009. [15] D. Vukobratovic and V. Stankovic, \Unequal error protection random linear coding for multimedia communications," in Proc. MMSP-2010 IEEE Multimedia and Signal Processing Workshop, Saint Malo, France, Oct. 2010. [16] S. Nazir, D. Vukobratovic, and V. Stankovic, \Expanding window random linear codes for data partitioned H.264 video transmission over DVB-H network," in Proc. ICIP-2011 IEEE Intl. Conf. Image Proc.,

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[17]Brussels, Belgium, Sept. 2011. A. B. Saleh, S. Redana, B. Raaf, T. Riihonen, J. Hämäläinen, and R. Wichman, “Performance of amplify-and-forward and decode-and forward relays in LTE-Advanced,” in Proc. IEEE Veh, Technol. Conf. (VTC-Fall), Sep. 2009. [18] I. Krikidis, J. Thompson, S. McLaughlin, and N. Goertz, “Amplifyand- forward with partial relay selection,” IEEE Commun. Lett., vol. 12, pp. 235–237, Apr. 2008. [19] R. Tannious and A. Nosratinia, “Spectrally-efficient relay selection with limited feedback,” IEEE J. Sel. Areas Commun., vol. 26, pp. 1419–1428, Oct. 2008. [20] T. C.-Y. Ng and W. Yu, “Joint optimization of relay strategies and resource allocations in cooperative cellular networks,” IEEE J. Sel. Areas Commun., vol. 25, pp. 328–339, Feb. 2007.

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Real-time Transmission of Layered MDC Video over Relay ... - IJRIT

MDC generates multiple descriptions of the same source data that can be ... In this paper, we propose a system for real-time video streaming to mobile users, via ...

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